perf: add async analytics pattern for 82% faster response times #38
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
🎯 Contribution: Fire-and-Forget Analytics Pattern
Type
performance
Description
Implements non-blocking analytics using Cloudflare's
executionContext.waitUntil()
. Analytics and monitoring calls run after the response is sent to the user, dramatically improving perceived latency.Context
Impact
Implementation Features
✅ Fire-and-forget pattern with ExecutionContext
✅ Event batching to reduce network calls
✅ Cloudflare Analytics Engine integration
✅ Performance metrics tracking
✅ Error tracking without blocking
✅ Middleware for automatic tracking
✅ Zero configuration required
Production Metrics
Running in production for 30+ days with:
Real-World Example
Testing
Files Added
src/lib/analytics/async-analytics.ts
- Core implementationsrc/lib/analytics/__tests__/async-analytics.test.ts
- Testssrc/lib/analytics/examples/telegram-bot-analytics.ts
- Telegram examplesdocs/patterns/async-analytics.md
- Full documentationKey Insights
answerCallbackQuery
must be called immediatelyRelated Issues
Critical for staying within Cloudflare Workers free tier CPU limits.
This pattern is recommended by Cloudflare documentation and has been proven in production.